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Record W2125319686 · doi:10.1111/1467-9280.00419

Truth and Character: Sources That Older Adults Can Remember

2002· article· en· W2125319686 on OpenAlexaff
Tamara A. Rahhal, Cynthia P. May, Lynn Hasher

Bibliographic record

VenuePsychological Science · 2002
Typearticle
Languageen
FieldNeuroscience
TopicMemory Processes and Influences
Canadian institutionsBaycrest HospitalUniversity of Toronto
FundersNational Institute on AgingU.S. Public Health Service
KeywordsPsychologyCharacter (mathematics)PerceptionStatement (logic)Information source (mathematics)Cognitive psychologyTask (project management)Social psychologyCommunicationDevelopmental psychologyLinguisticsNeuroscience

Abstract

fetched live from OpenAlex

Are age differences in source memory inevitable? The two experiments reported here examined the hypothesis that the type of source information being tested mediates the magnitude of age differences in source memory. In these studies, participants listened to statements made by two different speakers. We compared younger and older adults' source memory in a traditional perceptual source task (memory for voice) and in two affective, conceptually based source tasks (truth of the statements, character of a person in a photo). In both studies, the perceptual and conceptual source information were conveyed in the same manner, as one speaker was associated with one type of information (e.g., female voice speaks truth). Age differences were robust for decisions regarding who said each statement but were negligible or truth or character decisions. These findings are provocative because they suggest that the type of information can influence age-related patterns of performance for source-conveyed information.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.086
GPT teacher head0.323
Teacher spread0.237 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations205
Published2002
Admission routes1
Has abstractyes

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